TY - CHAP
T1 - Convolutional Neural Networks Do Work with Pre-Defined Filters
AU - Linse, Christoph
AU - Barth, Erhardt
AU - Martinetz, Thomas
PY - 2023
Y1 - 2023
N2 - We present a novel class of Convolutional Neural Networks called Pre-defined Filter Convolutional Neural Networks (PFCNNs), where all n× n convolution kernels with n > 1 are pre-defined and constant during training. It involves a special form of depthwise convolution operation called a Pre-defined Filter Module (PFM). In the channel-wise convolution part, the 1× n× n kernels are drawn from a fixed pool of only a few (16) different pre-defined kernels. In the 1× 1 convolution part linear combinations of the pre-defined filter outputs are learned. Despite this harsh restriction, complex and discriminative features are learned. These findings provide a novel perspective on the way how information is processed within deep CNNs. We discuss various properties of PFCNNs and prove their effectiveness using the popular datasets Caltech101, CIFAR10, CUB-200-2011, FGVC-Aircraft, Flowers102, and Stanford Cars. Our implementation of PFCNNs is provided on Github https://github.com/Criscraft/PredefinedFilterNetworks.
AB - We present a novel class of Convolutional Neural Networks called Pre-defined Filter Convolutional Neural Networks (PFCNNs), where all n× n convolution kernels with n > 1 are pre-defined and constant during training. It involves a special form of depthwise convolution operation called a Pre-defined Filter Module (PFM). In the channel-wise convolution part, the 1× n× n kernels are drawn from a fixed pool of only a few (16) different pre-defined kernels. In the 1× 1 convolution part linear combinations of the pre-defined filter outputs are learned. Despite this harsh restriction, complex and discriminative features are learned. These findings provide a novel perspective on the way how information is processed within deep CNNs. We discuss various properties of PFCNNs and prove their effectiveness using the popular datasets Caltech101, CIFAR10, CUB-200-2011, FGVC-Aircraft, Flowers102, and Stanford Cars. Our implementation of PFCNNs is provided on Github https://github.com/Criscraft/PredefinedFilterNetworks.
UR - https://www.mendeley.com/catalogue/18976dc8-2e56-332c-8e23-8629f8019fe1/
U2 - 10.1109/IJCNN54540.2023.10191449
DO - 10.1109/IJCNN54540.2023.10191449
M3 - Chapter
SN - 9781665488679
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - Proceedings of the International Joint Conference on Neural Networks
ER -